Multi-parametric Optimization and Control. Efstratios N. Pistikopoulos

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       Library of Congress Cataloging‐in‐Publication Data

      Names: Pistikopoulos, Efstratios N., author.

      Title: Multi‐parametric optimization and control / Efstratios N.

      Pistikopoulos, Nikolaos A. Diangelakis, Richard Oberdieck.

      Description: First edition. | Hoboken, NJ : Wiley, 2021. | Series: Wiley

      series in operations research and management science | Includes

      bibliographical references and index.

      Identifiers: LCCN 2020024011 (print) | LCCN 2020024012 (ebook) | ISBN

      9781119265184 (hardback) | ISBN 9781119265153 (adobe pdf) | ISBN

      9781119265191 (epub)

      Subjects: LCSH: Mathematical optimization–Computer programs.

      Classification: LCC QA402.5 .P558 2021 (print) | LCC QA402.5 (ebook) |

      DDC 519.7–dc23

      LC record available at https://lccn.loc.gov/2020024011

      LC ebook record available at https://lccn.loc.gov/2020024012

      Cover Design: Wiley

      Cover Image: Courtesy of Professor Pistikopoulos'research group

       To the Memory and Legacy of Professor Christodoulos A. Floudas

      Efstratios N. Pistikopoulos

      Richard Oberdieck is a Technical Account Manager at Gurobi Optimization, LLC, one of the leading mathematical optimization software companies. He obtained a bachelor and MSc degrees from ETH Zurich in Switzerland (2009–1013), before pursuing a PhD in Chemical Engineering at Imperial College London, UK, which he completed in 2017. During is PhD, he discovered fundamental properties of multi‐parametric programming problems and implemented them in the Parametric Optimization (POP) toolbox, of which he was the main developer. After using his knowledge in mathematical modeling and optimization in the space of renewable energies at the world leader in offshore wind energy, Ørsted A/S, he is now helping companies around the world unlock business value through mathematical optimization as a Technical Account Manager for Gurobi Optimization, LLC. He has published 21 papers and 2 book chapters, has an h‐index of 11 and was awarded the FICO Decisions Award 2019 in Optimization, Machine Learning and AI.

      Many optimization problems involve parameters that are unknown, either because they cannot be measured, or because they represent information about the future (e.g. future state of a system, future disturbance, future demand). Multi‐parametric programming is a technique for the solution of such class of uncertain optimization problems. Through multi‐parametric programming, one can obtain the optimization variables of the problem as a function of the bounded uncertain parameters, and the regions (in the space of the parameters) where these functions are valid.

      Theoretic and algorithmic developments on multi‐parametric programming, along with applications in the area of process systems engineering, have been constantly emerging during the last 30 years.

      A variety of algorithms for the solution of a range of classes of multi‐parametric programming problems have been developed, with our group publishing over 80 manuscripts, 21 books and book chapters, and 2 patents on the subject. We have further developed a MATLAB© based toolbox, POP©, for the solution of various classes of multi‐parametric programming and a framework, PAROC©, for the development of explicit model predictive controllers.

      The book begins with an introduction to the fundamentals of optimization and the basic theories and definitions used in multi‐parametric optimization. Then, two main parts follow, providing a clear distinction between algorithmic developments and their applications in the development

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